Lecture 13-14. More on Bayesian Estimation

Learning Goal: You should know and be able to use the Cramer-Rao Bound to understand fundamental limits on how well you can learn parameters $\theta$ from data $y$ obtained with probability $p(y;\theta)$. You should be able to describe and use the nonlinear Bayesian Filter and the  Kalman filter. You should also be able to describe and use a particle filter.

 

Reading Assignment:  Lecture notes on Bayesian Estimation andSensorFusion.pdf Download Lecture notes on Bayesian Estimation andSensorFusion.pdf

 

Lecture Slides: Lecture1314_BayesianEstimation.pdf Download Lecture1314_BayesianEstimation.pdf

Lecture Video: LECTURE1314_FULL.mp4 Download LECTURE1314_FULL.mp4Play media comment. (from 2020)

Also available on youtube here 

 

Code:

positionCRB.m Download positionCRB.m

gps.m Download gps.m

gps_tradeoff.m Download gps_tradeoff.m

PF.m Download PF.m

Please also check out the short video describing the particle filter available in the additional material:

Additional material - 13-14 Bayesian Estimation and Sensor Fusion